class_docstr: Find a linear mapping from uncalibrated activations to true activations.
  
  Should not change ev_correlation_score because it is invariant to linear transformations.
code: def __init__(self, uncalibrated_simulator: NeuronSimulator):
      super().__init__(uncalibrated_simulator)
      self._regression: Optional[linear_model.LinearRegression] = None
  
imports: from __future__ import annotations
  import asyncio
  from abc import abstractmethod
  from typing import Optional, Sequence
  import numpy
  from neuron_explainer.activations.activations import ActivationRecord
  from neuron_explainer.explanations.explanations import ActivationScale
  from neuron_explainer.explanations.simulator import NeuronSimulator, SequenceSimulation
  from sklearn import linear_model
label_desc: Use image node_id 1 to create a new LinearCalibratedNeuronSimulator object from inherited base classes: CalibratedNeuronSimulator with example: obj = LinearCalibratedNeuronSimulator(uncalibrated_simulator)